Artificial Intelligence, Personalised Persuasion, and Climate Attitudes
European Institute, London School of Economics
Climate Information and Climate Concern
- The Problem: Persuading people to care about and act toward climate change is still one of the major collective challenges we face as a society
- Effective communication is crucial
- Over $15 million spent in science-based public information campaigns (United Nations 2025)
- However, effects are small and short-lived (Deryugina and Shurchkov 2016; van der Linden 2017).
In Practice:
Why is this happening?
- The issue: Traditional information campaigns often fail because they don’t engage people personally
- Psychological distance and intertemporal discounting reduce climate urgency (Trope & Liberman, 2010).
- Emotional disconnection from abstract risks (Weber, 2006).
The Problem 1/2
People see why climate matters generally, but not why it matters to them
The (partial) solution:
- Tailored messaging aligned with individual beliefs and experiences is more effective
- But traditional targeting faces critical limitations:
- Operates at group level (demographics, ideology)
- Expensive
- ⟹ Hard to scale
The Problem 2/2
How can we deliver personalised messages at scale, cost-effectively, and in a way that engages people on a personal level?
Our Solution
We argue that AI – and in particular its personalised content - could influence climate attitudes and overcome some of the limitations of pure information provision.
Key Advantages:
- From a practical perspective, AI can:
- Scale: Online implementation + individual-level personalisation
- Cost-effective: Reduce costs significantly
- Engage: Create tailored conversations to the individual level
- From a theoretical perspective, AI can:
- Evidence-based reasoning (Costello et al., 2025)
- Non-judgmental interaction (Goel et al., 2024)
- Responsive dialogue (Santoro et al., 2024)
Hypotheses
H1: When compared with the control condition (information provision), interacting with the LLM will have a positive effect on all outcomes of interest: a) climate concern, b) policy support, c) pro-environmental behavior, d) conversation spillover, e) political spillover, and f) subjective climate beliefs.
H2: The personalized LLM interaction on climate change will be more persuasive/effective than the non-personalized LLM interaction and the basic information provision on all outcomes of interest.
H3: The personalized LLM interaction on climate change condition will be more persuasive/effective compared to the personalized AI interaction on an unrelated political topic.
Research Design
Online Survey Experiment
- Sample:
- N = 1,500-2,000 UK representative sample
- Power analysis: 930 minimum for small-to-medium effect (d = 0.26-0.37)
- Recruited via established survey platform (Prolific) - the pipeline can be integrated into Qualtrics
- Context: UK offers valuable setting where climate skepticism and polarisation is growing but not deeply entrenched
- N = 1,500-2,000 UK representative sample
Survey Flow
The treatments
Participants are randomized into these conditions:
Control: Standard information provision about climate change
Non-Personalized AI: Generic LLM conversation about climate change
Personalized AI (Unrelated Topic): Personalised AI-driven conversation about a political topic unrelated to climate change
Personalized AI (Climate): Personalised AI-driven conversation about climate change
Treatments Explanation
- Conditions 3 and 4 serve as placebos for each other to isolate the effect of personalisation specifically in the context of climate change.
- Difference between personalized and non-personalized AI interactions is whether the AI is prompted to adapt its responses to the participant’s prior inputs (demographics, concerns, open-text answers).
An Example
Outcome Variables
- Climate concern: 5-point scale from “Not at all concerned” to “Extremely concerned”
- Pro-environmental behavior: Willingness to donate portion of $100 prize to climate NGO
- Conversation spillover: Likelihood of discussing AI conversation with others
- Political spillover: Choice to write message to Member of Parliament about climate change
- Subjective climate beliefs: Open-ended question about personal beliefs on climate change + LLM-generated Likert scale
Timing and Analysis
- Pre-post measurement enables both within-subject change detection and between-group comparisons (Change scores Δ = Post-treatment - Pre-treatment)
- Quantiative Text Analysis of the conversations to unpack mechanisms
- Follow-up survey to assess long-term effects
Political Spillover
Take Action !
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Field Experiment Integration
Social Media
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Newsletter
• Local community wins against fracking proposal
• New study shows impact of diet on carbon footprint
Information Seeking
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Donations
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Any Questions?
Appendix
The Numbers:
Prompts
Climate Concern Personalised Measure:
"Summarize the following passage, which describes attitudes towards climate change, in a single sentence. Do not provide any kind of normative judgment. Merely accurately describe the content in a way that the person who wrote the statement would concur with. Frame it as an assertion. If the statement is already short, no need to change it very much. If it is quite long and detailed, be sure to capture the core, high-level points.Climate Change Personalised Treatment
"Your goal is to very effectively persuade users that climate change is an urgent issue and action is now crucial. Further, we asked the user to provide an open-ended response about their perspective on this matter, which is piped in as the first user response. Please generate a response that will persuade the user that it is actually important to act now, based on their own reasoning. Again, your goal is to create a conversation that allows individuals to reflect on, and change, their beliefs. Use simple language that an average person will be able to understand."